In crisp logic every object is similar to itself with degree of reflexivity 1, while the degree of reflexivity in fuzzy logic can be any value in the unit interval, [0, 1]. This behaviour of a fuzzy set is used to enlighten the concept of similarity and inclusion measures. This paper is aimed at discovering the relations between the parameters of the transitive members of a family of cardinality-based fuzzy measure.
People are often facing the situations in which they prefer one object over the other or they compare them in their daily life on the basis of features or attributes. This process is commonly known as similarity and inclusion measures. Most of the similarity measures are originated from Taxonomy [28], where feature-based approach is followed. While studying these measures (similarity and inclusion) Mathematically, the objects are normally represented by the sets of features. For example, when comparing two cars these sets may contain their colors, safety ratings and their performances. Therefore, similarity and inclusion measures play a vital role in organizing and classifying the objects, plants, species, etc. [18, 24] and in the association between the species [13, 25]. It is a very important concept in many scientific fields such as Bioinformatics, Biology, Chemistry, Information retrieval, Statistics and many others [3, 7, 17, 27, 29, 30] and [32].
Similarity measure determines the degree of resemblance between two objects while the inclusion measure expresses the degree to which the characteristics of the one object include another. These measures received much more attention in recent decades because of their application in decision making, pattern recognition, medical diagnosis and data mining applications.
Lotfi A. Zadeh introduced the notion of fuzzy sets [35] in 1965, which have an edge over the crisp sets that they can represent the degrees of truth or falsehood only. Fuzzy set define the intermediate values between the complete truth and complete false and hence fuzzy measures. Consequently, in many practical applications, fuzzy similarity measures which represent the degree of similarity between two objects seem much closer to reality than their crisp counterparts do. Since their inception in 1973, fuzzy similarity and inclusion measures found almost all application areas from A to Z (i.e., from Anthropology to Zoology). Cardinality based similarity and inclusion measures play a key role in literature and applications [1, 15] and [34]. Some memorable contributions can be found in [2, 8–10, 20] and [21].
Cardinality based similarity and inclusion measures rely on the cardinalities of sets of common and different features. New measures were introduced by assigning different weights to the sets of common and different features. Such families of similarity and inclusion measures are known as parametric families and these weights are termed as parameters. Some famous parametric families were introduced by Tversky [30], Gower & Legendre [16] and De Baets et al. [9]. A family of rational expressions using two, three and four parameters [4, 12] and [20] are available in literature. While axioms for fuzzy similarity measures are focused, fuzzy equivalence relation stands as the best model for fuzzy similarity measure. A fuzzy equivalence relation is reflexive, symmetric and T-transitive [31] fuzzy relation. Unfortunately, the majority of fuzzy similarity measures found in literature do not follow one or the other desired form of fuzzy transitivity, in particular, the axiom of T-transitivity is violated. In [20] Janssens et al. established conditions under which a parametric family of cardinality based fuzzy similarity measures become T-transitive. The significant development of meta-theorems, which ensure T-transitivity, can be found in [11] and [21]. These theorems are used to construct the necessary and sufficient conditions to obtain transitivity.
De Baets et al introduced a parametric family of cardinality based similarity measure [8] and inclusion measure [9], which cover most of the famous measures found in literature for different values of parameters. Basically, these measures used eight parameters and Janssens et al. characterized the T-transitive members of the family of similarity and inclusion measures for only four parameters for Łukasiewicz, Product and Min t-norms.
Sometimes in similarity or inclusion measure, the features which are absent or negative match play a key role in the comparison of two objects and it seems improper to ignore these negative match features. In this context, a manuscript is already been submitted by Javed, Samina and Syed using six parameters to characterize the Łukasiewicz-transitive members and fuzzy reflexive measures using Łukasiewicz t-norm is accepted [19] while this submission not only tried to explore the transitivity between the objects based on the similarities but also to incorporate the negative matching features. This paper is focused to determine the necessary conditions while extending the number of parameters to eight for Łukasiewicz-, Product- and Min-transitive family of similarity and inclusion measures based on cardinality.
By using four parameters, the family introduced by De Baets et al covers some of the inclusion and similarity measures available in literature [9, 33] but fails to define some measures like Kuncheva [23]. This shortcoming can be cover by extending the number of parameter. So, it is an effort to include some more measures in this parametric family, which were put out of action by the restriction of four parameters.
The notion of fuzzy set was introduced by Zadeh in 1965 in his seminal paper [35]. A fuzzy set A is a mapping from a universe X to [0, 1]. For any x ∈ X, the value A (x) denotes the degree of membership of x in A. Let F (X) be the set of all fuzzy subsets of a universe X. For a crisp universe X, a fuzzy subset of X × X is called a fuzzy binary relation and throughout this paper we termed fuzzy binary relations as fuzzy relations. Given a crisp universe X, and A, B ∈ F (X), A is said to be a subset of B (in Zadeh’s sense [35]) denoted by A ⊆ B, if and only if A (x) ≤ B (x) for all x ∈ X.
Definition 1.1. [26] The triangular norm (t-norm) T and triangular conorm (t-conorm) T∗ are increasing, associative, commutative and mapping [0, 1] 2 → [0, 1] satisfying T (1, x) = x and T∗ (x, 0) = x for all x ∈ [0, 1].
To every t-norm T there corresponds a t-conorm T∗ called the dual t-conorm, defined by: T∗ (x, y) =1 - T (1 - x, 1 - y) . For the Lukasiewicz t-norm W (x, y) = max(x + y - 1, 0) , the corresponding t-conorm is W∗ (x,) = min(x + y, 1) .
Definition 1.2. [14] A negator N is an order-reversing [0, 1] → [0, 1] mapping such that N (0) =1 and N (1) =0. A strictly decreasing negator satisfying N (N (x)) = x for all x ∈ [0, 1] is called a strong negator.
The negator defined as: N (x) =1 - x for all x ∈ X, is called standard negator and was defined by Zadeh himself.
Definition 1.3. [31] Given a t-norm T, a T-equivalence relation on a set X is a fuzzy relation E on X that satisfies:
E (x, x) =1 for all x ∈ X; (Reflexivity),
E (x, y) = E (y, x) for all x, y ∈ X; (Symmetry),
T (E (x, y) , E (y, z)) ≤ E (x, z) for all x, y, z ∈ X; (T-transitivity).
Definition 1.4. [14] Let R be a fuzzy relation on X . A triangular norm T is T-transitive if and only if for all x, y, z ∈ X, T (R (x, y) , R (y, z)) ≤ R (x, z) .
The Lukasiewicz TL- and Product TP-transitive similarity measures are of special interests. A similarity measure S is Lukasiewicz-transitive if it satisfies
for all A, B, C ∈ P (X). A similarity measure S is Product-transitive if it holds
for all A, B, C ∈ P (X). Similarly, a measure S is Min-transitive if it holds
for all A, B, C ∈ P (X).
Definition 1.5. [31] An inclusion measure for ordinary sets is binary fuzzy relation I on the power set P (X) = { 0, 1 } X satisfying, A ⊂ B ⇒ I (A, B) = 1.
Transitivity of inclusion measures
In 2001, De Baets et al. [8] introduced a rational cardinality based inclusion measure. This parametric family contains eight parameters to define inclusion measure for two subsets A and B in a finite universe X and measure the degree of inclusion of one set in the other set as
with χA,B = |A ∖ B|, χB,A = |B ∖ A|, δA,B = |A ∩ B|, νA,B = | (A ∪ B) c|, etc. and the parameters x, x′, t, t′, y, y′, z, z′ ∈ {0, 1}. These parameters are considered to be positive real numbers. The conditions of x < x′ and t < t′ are imposed to contain inclusion measure I (A, B) in unit interval, as x = x′, t = t′ leads to the trivial case, so we consider 0 ≤ x < x′ and 0 ≤ t < t′.
Cardinalities associated with sets A, B and C.
Janssens et al. [20] restricted the number of parameters of the family to four parameters x, x′, y and z and explore the necessary and sufficient condition on the parameters for T-transitivity. In this section, we determine the conditions on parameters by extending their number up to eight parameters x, x′, t, t′, y, y′, z and z′ for T-transitivity. For this purpose, the notion of fuzzy reflexivity of a measure is used. The inclusion measure will be fuzzy reflexive if y′ = θy, z′ = φz for θ, φ ≥ 1, so the above inclusion measure can be written as
with θ, φ ≥ 1. For six parameters, the above inclusion measure can be written as
where y′ = θy, z′ = φz for θ, φ ≥ 1 and ▵A,B = |A ∖ B| + |B ∖ A|. Consider the setting in figure, then following conditions hold,
where ai′s, bi′s, c and d are cardinalities. In this paper, Lukasiewicz, Product and Min t-norms are used to articulate the conditions for transitivity, which are termed as TL - , TP- and TM-transitivity throughout the paper.
Lukasiewicz transitive members
Theorem 2.1.1.The TL-transitive members of the class of inclusion measures (ref equ2.03) are characterized by the necessary condition
Proof. The inclusion measure I is TL-transitive if it fulfill the definition of transitivity (1). To determine the conditions on parameters x, x′, y, and z for transitivity, the inequality (1) can be written in terms of family (6) as
Using the values of cardinalities given in (7), the above inequality implies
Setting a1 = b1 = c = d = 0, (8) implies
Since x′ > x, θ ≥ 1 and φ ≥ 1, so the factors x′ - x, θ - 1 and φ - 1 can be omitted and we obtain
Expanding the above inequality and setting a2 = a3 = 0 leads to (x′) 2 - (x′ - yθ) yθ ≥ 0 which can be satisfied when x′ ≥ yθ . Other combinations of ai′s and bi′s, that is, b2 = b3 = 0, a2 = b3 = 0, or b2 = a3 = 0 do not lead to any other conditions on x, x′, y, z. Setting a2 = b2 = c = d = 0 in (8). Since x′ > x, θ ≥ 1 and φ ≥ 1 so the factors x′ - x, θ - 1 and φ - 1 can be omitted and we obtain
In particular, setting a1 = a3 = 0 in the above inequality, it will be true if x′ ≥ yθ. Similarly, by setting b1 = b3 = 0, that is, x′ ≥ zφ is necessary to hold the inequality and setting of b1 = a3 = 0, ends up with a weaker condition (x′) 2 - yzθφ ≥ 0 . Other combinations of ai′s and bi′s do not lead to any other conditions on x, x′, y, z. Setting a3 = b3 = c = d = 0 in (8). Since x′ > x, θ ≥ 1 and φ ≥ 1 so the factors x′ - x, θ - 1 and φ - 1 can be omitted
In particular, setting b1 = b2 = 0 and expand the inequality leads to the result (x′) 2 - (x′ - zφ) zφ ≥ 0 which will be true only if x′ ≥ zφ and no other combinations of ai′s and bi′s lead to other conditions on x, x′, y and z . Thus x′ ≥ max(yθ, zφ) as the necessary conditions for the family to be TL-transitivity.
Corollary 2.1.2.If θ = φ, then TL-transitive members of the class of inclusion measure (6) are characterized by the necessary conditions
Corollary 2.1.3.If θ = 1 = φ, then TL -transitive members of the class of inclusion measure (6) are characterized by the necessary conditions
Theorem 2.1.4.The TL-transitive members of the class of inclusion measures (6) are characterized by the necessary conditions
Proof. To determine the conditions on parameters x, x′, t, t′, y, and z for transitivity, the inequality (1) can be written in terms of family (6), as
Considering the settings in figure and using the values of cardinalities given in (7), the above inequality leads to
Setting a1 = b1 = c = d = 0 in (8) and ignoring the factors x′ - x, t′ - t, θ - 1 and φ - 1 leads to
and
In particular, setting a2 = a3 = 0 in inequality (11) implies
and setting b2 = b3 = 0 in inequality (12) implies
Other combinations of ai′s and bi′s do not provide any other condition on x, x′, t, t′, y, z. Setting a2 = b2 = c = d = 0 in (10). As x′ > x, t′ > t, θ ≥ 1 and φ ≥ 1, so the factors x′ - x, t′ - t, θ - 1 and φ - 1 can be omitted and this leads to the inequality (15) and the inequality (16),
and
In particular, setting a1 = a3 = 0 and b1 = b3 = 0 in inequality (15) implies t′ - yθ ≥ 0 and t′ - zφ ≥ 0 repectively and a3 = b1 = 0 in (15) leads to the weak conditions (x′) 2 ≥ yzθφ . Similarly by setting a1 = a3 = 0 and b1 = b3 = 0 in (16) impliesand a1 = b3 = 0 in inequality (16) gives a weaker condition (t′) 2 ≥ yzθφ . Thus inequalities (15) and (16) will be true only if t′ ≥ max(yθ, zφ) and x′ ≥ max(yθ, zφ). That is
Setting a3 = b3 = c = d = 0 in (10). Using x′ > x, t′ > t, θ ≥ 1 and φ ≥ 1 so the factors x′ - x, t′ - t, θ - 1 and φ - 1 can be omitted and this leads to the inequality (18) and the inequality (19),
and
In particular, setting a1 = a2 = 0 in the inequality (19) implies
In particular, setting b1 = b2 = 0 in the inequality (18) implies
Other combinations of ai′s and bi′s do not provide any other condition on x, x′, t, t′, y, z. Above conditions (13) and (20) can be combine to
and conditions (14) and (2.1) can be combine to
Product transitive members
Theorem 2.2.1.TheTP-transitive members of the class of inclusion measures (6) are characterized by the necessary conditions
Proof. To determine the conditions on parameters x, x′, y, and z for transitivity, the inclusion measure I is TP-transitive if it holds the inequality (2). Then the inequality (2) can be written in terms of inclusion measure (6),
Using the values of cardinalities given in (7), the above inequality implies
Setting a1 = b1 = c = d = 0, (21) implies
Since x′ > x, θ ≥ 1 and φ ≥ 1, so the factors x′ - x, θ - 1 and φ - 1 can be omitted and we obtain
Expanding the above inequality and setting a2 = a3 = 0 leads to
and setting a3 = b2 = 0 leads to
Other combinations of ai′s and bi′s, that is, b2 = b3 = 0 or a2 = b3 = 0 do not lead to any other conditions on x, x′, y, z. Setting a2 = b2 = c = d = 0, in (21) and ignoring the factors x′ - x, θ - 1 and φ - 1, we obtain
In particular, setting a1 = a3 = 0 in inequality (24) leads to the result
which is true if x′ ≥ yθ. Similarly, setting of b1 = b3 = 0 in inequality (24) implies
that is, x′ ≥ zφ is necessary to hold the inequality and setting of b1 = a3 = 0 in (24) ends up with a condition
that is,
Other combinations of and do not lead to any other conditions on x, x′, y, z. Setting a3 = b3 = c = d = 0 in (21) and factors x′ - x, θ - 1 and φ - 1 can be omitted. Thus
In particular, setting b1 = b2 = 0 and expand the inequality leads to the result
and setting a2 = b1 = 0 leads to the result
Other combinations of ai′s and bi′s do not lead to other conditions on x, x′, y and z.
Theorem 2.2.2.The TP-transitive members of the class of inclusion measures (5) are characterized by the necessary conditions
Proof. To determine the conditions on parameters x, x′, y, and z for transitivity, let us suppose that the inclusion measure S is TP-transitive i.e.,
Considering the settings in figure and using the values of cardinalities given in (7), the above inequality leads to
Setting a1 = b1 = c = d = 0 in (31) and ignoring the factors x′ - x, t′ - t, θ - 1 and φ - 1, the inequality can be simplified as (32)
and as (33)
In particular, setting a2 = a3 = 0 in inequality (34) implies
and setting b2 = a3 = 0 in inequality (35) implies
and setting b2 = b3 = 0 in inequality (37) implies
and setting a2 = b3 = 0 in inequality (33) implies
Other combinations of ai′s and bi′s do not provide any other condition on x, x′, t, t′, y, z. Setting a2 = b2 = c = d = 0 (31) and the factors x′ - x, t′ - t, θ - 1 and φ - 1 can be omitted and this leads to the inequality (40) and the inequality (41),
and
In particular, setting a1 = a3 = 0, b1 = b3 = 0 and a3 = b1 = 0 in inequality (40) implies
Similarly by setting a1 = a3 = 0, b1 = b3 = 0 and a1 = b3 = 0 in (41) implies
Setting a3 = b3 = c = d = 0 (31) and the factors x′ - x, t′ - t, θ - 1 and φ - 1 can be omitted and this leads to the inequality (42) and the inequality (43),
and
In particular, setting a1 = a2 = 0 in the inequality (43) implies
and setting a1 = b2 = 0 in the inequality (43) implies
In particular, setting b1 = b2 = 0 in the inequality (42) implies
and setting a2 = b1 = 0 in the inequality (42) implies
Other combinations of ai′s and bi′s do not provide any other condition on x, x′, t, t′, y, z.
Min transitive members
Theorem 2.3.1.The family (6) does not contain any TM-transitive members.
Proof. The inclusion measure I is TM-transitive if it fulfill the definition of transitivity (3). For this purpose, we have to verify that either I (A, B) ≤ I (A, C) or I (B, C) ≤ I (A, C). That is,
Using the values of cardinalities given in (7), the above inequality implies
Setting a2 = b2 = c = d = 0 in (42) and ignoring the factors x′ - x, θ - 1 and φ - 1, the inequality can be simplified as
On simplification, this leads to contradiction as
Remark 2.3.2. The family (5) does not contain any TM transitive members.
Transitivity of similarity measures
A common approach for comparing two objects is to select an appropriate list of features then prepare a binary vector of {0, 1} based on the fact that if the object has certain feature will be recorded as 1 and 0 otherwise. The degree of similarity of the two objects is then expressed in terms of cardinalities of these binary vector sets A and B in a finite universe X as
with A, B ∈ P (X) , ωA,B = max(|A ∖ B|, |B ∖ A|) , αA,B = min(|A ∖ B|, |B ∖ A|) , δA,B = |A ∩ B|, νA,B = | (A ∪ B) c| and parameters x, x′, t, t′, y, y′, z, z′ ∈ {0, 1} . The similarity measure is symmetric, that is, for all A, B ∈ F (X) , S (A, B) = S (B, A) and fuzzy reflexive for any A ∈ F (X) , S (A, A) ∈ [0, 1]. Janssens et al. [22] used four parameters to test the various properties of monotonicity and transitivity for Łukasiewicz, Product and Min t-norms. Some members of the family (53) with parameters discussed by Janssens [22] in the light of eight parameters are given in Table 2. Many other measures cannot be discussed under parametric family of similarity measures using four parameters, some of them are given in Table 3. Some of these measures are explored in this section by using all eight parameters. Fuzzy similarity measure will hold fuzzy reflexivity for y′ = yθ, z′ = zφ with θ, φ ≥ 1, and the above relation of similarity measure can be written as
For simplicity, if we use only four parameters, that is, x = t, x′ = t′, the above relation becomes
which is an extension of four parameters of Janssens’ to six parameters with ΔA,B = |AΔB|.
These similarity measures are symmetric for any fuzzy subsets A, B and will be fuzzy reflexive if y′ = yθ and z′ = zφ for θ, φ ≥ 1 . That is with positive real parameters x, x′, t, t′, y, z . S (A, B) ∈ [0, 1] if 0 ≤ x < x′ and 0 ≤ t < t′. This section is focused to characterize the Łukasiewicz-transitive members of family (53).
Lukasiewicz transitive members
Theorem 3.1.1.The TL-transitive members of family (equ3.03) are characterized by the necessary condition
Proof. To determine the conditions on parameters x, x′, y, and z for transitivity, the similarity measure S is TL-transitive if it holds the inequality (equ1.1). In terms of family (equ3.03), the inequality can be written as
Consider the setting in figure, and substituting the cardinalities (equ2.04), the above inequality implies
Similarly setting a2 = b2 = c = d = 0 and omitting the factors x′ - x, θ - 1 and φ - 1, the inequality (equ31.11) implies that
Now discussing the above inequality, by setting a1 = a3 = 0, the inequality leads to the result (x′) 2 - y2θ2 ≥ 0 which is fulfilled only if x′ ≥ yθ and setting b1 = b3 = 0, the inequality leads to (x′) 2 - z2φ2 ≥ 0, which is satisfied if x′ ≥ zφ. Similarly, setting a1 = b3 = 0 or b1 = a3 = 0 in the inequality leads to the weaker condition (x′) 2 - yzθφ ≥ 0. These conditions can be combine into a single condition as
Setting a1 = b1 = c = d = 0, and a3 = b3 = c = d = 0 the inequality (equ31.11) does not imply any further condition on x, x′, y and z.
Theorem 3.1.2.The TL-transitive members of family (equ3.02) are characterized by the necessary condition
Proof. To determine the conditions on parameters x, x′, t, t′, y, and z for transitivity, the similarity measure S is TL-transitive if it holds In terms of family (equ3.02), the above inequality can be written as
Consider the setting in figure, and substituting the cardinalities (equ2.04), the above inequality implies
Setting a1 = b1 = c = d = 0 in the inequality (equ31.21) and ignoring the factors x′ - x, θ - 1 and φ - 1, we obtain
and
In particular, setting a2 = a3 = 0, the inequality (equ31.22) leads to the results
and setting b2 = b3 = 0, the inequality (equ31.23) leads to the results
Other combinations of ai′s and bi′s do not provide any other condition on x, x′, t, t′, y, z. Similarly setting a2 = b2 = c = d = 0, the inequality (equ31.21) implies that
and
In particular, setting a1 = a3 = 0 and b1 = b3 = 0 the inequality (equ31.26) leads to the results t′ - yθ ≥ 0 and t′ - zφ ≥ 0 respectively, which can be written in the form
Also setting a1 = a3 = 0 and b1 = b3 = 0, the inequality (equ31.27) leads to the results x′ - yθ ≥ 0 and x′ - zφ ≥ 0 respectively, which can be combine in the form
The results (equ31.28) and (equ31.29) leads to
Similarly setting a3 = b3 = c = d = 0, the inequality (equ31.21) implies that
and
In particular, setting a1 = a2 = 0,the inequality (equ31.212) leads to the result
and setting b1 = b2 = 0,the inequality (equ31.211) leads to the result
Now results (equ31.24) and (equ31.214) leads to
and results (equ31.25) and (equ31.213) merged to
Thus the necessary condition for transitivity is obtained by (equ31.210, equ31.215 and equ31.216) as
Product transitive members
Theorem 3.2.1.The TP-transitive members of family (equ3.03) are characterized by the necessary condition
Proof. To determine the conditions on parameters x, x′, y,and z for transitivity, the similarity measure S is TP-transitive if it holds the inequality (equ1.2). In terms of family (equ3.03), the inequality (equ1.2) can be written as
Consider the setting in figure, and substituting the cardinalities (equ2.04), the above inequality implies
Setting a2 = b2 = c = d = 0, the inequality (equ32.11) implies that
In particular, setting a1 = a3 = 0,the above inequality leads to the result xx′ - y2θ2 ≥ 0 which is fulfilled only if xx′ ≥ y2θ2 and setting b1 = b3 = 0,the inequality leads to the result xx′ - z2φ2 ≥ 0 which is satisfied if xx′ ≥ z2φ2. Similarly, setting a1 = b3 = 0,and b1 = a3 = 0,in the inequality as xx′ - yzθφ ≥ 0 lead to the result xx′ ≥ yzθφ. These conditions can be combine into a single condition as
Setting a1 = b1 = c = d = 0,and a3 = b3 = c = d = 0 the inequality (equ32.11) does not imply any further condition on x, x′, y and z.
Theorem 3.2.2.The TP-transitive members of family (equ3.02) are characterized by the necessary condition
Proof. To determine the conditions on parameters x, x′, t, t′, y,and z for transitivity, the similarity measure S is TP-transitive if it fulfill the condition (equ1.2) which can be written in terms of family (equ3.02), as
Consider the setting in figure, and substituting the cardinalities (equ2.04), the above inequality implies
Setting a1 = b1 = c = d = 0 and omitting the factors x′ - x, t′ - t, θ - 1 and φ - 1 the inequality (equ32.21) leads to
and
In particular, setting a2 = a3 = 0,the inequality (equ32.22) leads to the results
and setting b2 = a3 = 0,the inequality (equ32.22) leads to
and setting b2 = b3 = 0,the inequality (equ32.23) leads to the results
and setting a2 = b3 = 0,the inequality (equ32.23) leads to
Other combinations of ai′s and bi′s do not provide any other condition on x, x′, t, t′, y, z. Similarly setting a2 = b2 = c = d = 0 in the inequality (equ32.21), we get
and
In particular, setting a1 = a3 = 0 and b1 = b3 = 0 the inequality (equ32.212) leads to the results
and
respectively. Similarly setting a1 = b3 = 0 provides
and setting of b1 = a3 = 0 gives
Also setting a1 = a3 = 0 and b1 = b3 = 0,the inequality (equ32.212) leads to the results
and
respectively. Similarly setting a3 = b3 = c = d = 0,the inequality (equ32.21) leads to
and
In particular, setting a1 = a2 = 0 and a1 = b2 = 0 the inequality (equ32.220) leads to the results
and the inequality
will lead to same result (equ32.211) and setting b1 = b2 = 0 and b1 = a2 = 0 the inequality (equ32.219) leads to the results
and the inequality
will ends up with same result as (equ32.27). Now inequalities (equ32.24) and (equ32.222) will be true only if
and inequalities (equ32.28),(equ32.217), (equ32.218) and (equ32.221) will be satisfied only if
That is,
Thus (equ32.25, equ32.26, equ32.27, equ32.29, equ32.210,equ32.211equ32.214,equ32.216 andequ32.225) are the necessary condition for transitivity.
Min transitive members
Theorem 3.3.1.The family (equ3.03) does not contain any TM-transitive members.
Proof. The inclusion measure I is TM-transitive if it fulfill the definition of transitivity (equ1.3). For this purpose, we have to verify that either I (A, B) ≤ I (A, C) or I (B, C) ≤ I (A, C). That is,
Using the values of cardinalities given in (equ2.04), the above inequality implies
Setting a2 = b2 = c = d = 0 in (equ33.11) and ignoring the factors x′ - x, θ - 1 and φ - 1, the inequality can be simplified as
On simplification, this leads to contradiction as
Remark 3.3.2. The family (6) does not contain any TM transitive members.
Conclusion
In this paper, we try to tackle the problem arises due the limitation imposed by four parameters and try to discuss the necessary conditions for transitivity of similarity and inclusion measure. This would help to compare objects based not only on positive matching but also on the basis of negative matchings. This will play a significance role medicines, image processing, text mining, etc. where negative match has a vital role. For future directions, similar parametric family can be devised for multiplication of cardinalities.
Footnotes
Acknowledgments
The authors want to thank the referees for their valuable comments that have contributed to an improvement of the paper.
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